Overview

Dataset statistics

Number of variables25
Number of observations1128
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory261.4 KiB
Average record size in memory237.3 B

Variable types

Numeric23
Categorical2

Alerts

precinct has a high cardinality: 1127 distinct valuesHigh cardinality
votes_for_candidate_I is highly overall correlated with votes_for_candidate_U and 7 other fieldsHigh correlation
votes_for_candidate_U is highly overall correlated with votes_for_candidate_I and 10 other fieldsHigh correlation
population is highly overall correlated with votes_for_candidate_I and 7 other fieldsHigh correlation
votes_for_president is highly overall correlated with votes_for_candidate_I and 8 other fieldsHigh correlation
registered_voters is highly overall correlated with votes_for_candidate_I and 7 other fieldsHigh correlation
all_ballot_measure_votes is highly overall correlated with votes_for_candidate_I and 8 other fieldsHigh correlation
total_ballots is highly overall correlated with votes_for_candidate_I and 8 other fieldsHigh correlation
votes_against_ballot_measure is highly overall correlated with votes_for_candidate_I and 8 other fieldsHigh correlation
votes_for_ballot_measure is highly overall correlated with votes_for_candidate_I and 7 other fieldsHigh correlation
probability_race_G is highly overall correlated with probability_race_O and 9 other fieldsHigh correlation
probability_race_P is highly overall correlated with ageHigh correlation
probability_race_O is highly overall correlated with probability_race_G and 4 other fieldsHigh correlation
age is highly overall correlated with probability_race_G and 9 other fieldsHigh correlation
partisan_score is highly overall correlated with votes_for_candidate_U and 8 other fieldsHigh correlation
turnout_score is highly overall correlated with probability_race_G and 8 other fieldsHigh correlation
probability_highest_education_high_school is highly overall correlated with probability_race_G and 2 other fieldsHigh correlation
support_tax_on_wealthy_score is highly overall correlated with votes_for_candidate_U and 9 other fieldsHigh correlation
support_progressive_taxation_score is highly overall correlated with probability_race_G and 6 other fieldsHigh correlation
support_cannabis_legalization_score is highly overall correlated with probability_race_G and 6 other fieldsHigh correlation
probability_income_over_100k is highly overall correlated with votes_for_candidate_U and 8 other fieldsHigh correlation
probability_children_in_household is highly overall correlated with ageHigh correlation
support_trump_score is highly overall correlated with probability_race_G and 5 other fieldsHigh correlation
precinct is uniformly distributedUniform

Reproduction

Analysis started2023-02-01 05:29:31.940767
Analysis finished2023-02-01 05:30:28.086386
Duration56.15 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

votes_for_candidate_I
Real number (ℝ)

Distinct903
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125.1817
Minimum0
Maximum5263
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:28.161456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile86.35
Q1524.25
median1003
Q31606.25
95-th percentile2483.4
Maximum5263
Range5263
Interquartile range (IQR)1082

Descriptive statistics

Standard deviation786.64627
Coefficient of variation (CV)0.69912819
Kurtosis1.6787949
Mean1125.1817
Median Absolute Deviation (MAD)528
Skewness0.98864403
Sum1269205
Variance618812.35
MonotonicityNot monotonic
2023-01-31T23:30:28.285568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 4
 
0.4%
1620 4
 
0.4%
811 4
 
0.4%
1286 3
 
0.3%
1096 3
 
0.3%
698 3
 
0.3%
900 3
 
0.3%
1128 3
 
0.3%
1080 3
 
0.3%
930 3
 
0.3%
Other values (893) 1095
97.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
10 1
 
0.1%
12 2
0.2%
13 4
0.4%
14 2
0.2%
15 1
 
0.1%
ValueCountFrequency (%)
5263 1
0.1%
5153 1
0.1%
5059 1
0.1%
3977 1
0.1%
3889 1
0.1%
3776 1
0.1%
3733 1
0.1%
3632 1
0.1%
3572 1
0.1%
3498 1
0.1%

votes_for_candidate_U
Real number (ℝ)

Distinct910
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1112.1986
Minimum0
Maximum18024
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:28.401673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.35
Q1411.75
median847.5
Q31455.25
95-th percentile3203.75
Maximum18024
Range18024
Interquartile range (IQR)1043.5

Descriptive statistics

Standard deviation1182.7724
Coefficient of variation (CV)1.0634543
Kurtosis48.17847
Mean1112.1986
Median Absolute Deviation (MAD)491
Skewness4.785543
Sum1254560
Variance1398950.5
MonotonicityNot monotonic
2023-01-31T23:30:28.502765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 4
 
0.4%
174 4
 
0.4%
762 4
 
0.4%
1033 4
 
0.4%
931 4
 
0.4%
699 4
 
0.4%
5 4
 
0.4%
211 3
 
0.3%
856 3
 
0.3%
377 3
 
0.3%
Other values (900) 1091
96.7%
ValueCountFrequency (%)
0 1
 
0.1%
2 2
0.2%
5 4
0.4%
7 2
0.2%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
14 1
 
0.1%
16 3
0.3%
ValueCountFrequency (%)
18024 1
0.1%
12804 1
0.1%
8479 1
0.1%
6458 1
0.1%
6424 1
0.1%
6217 1
0.1%
5988 1
0.1%
5872 1
0.1%
5725 1
0.1%
5512 1
0.1%

county
Categorical

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
county__1
563 
county__3
193 
county__11
73 
county__6
58 
county__2
 
36
Other values (10)
205 

Length

Max length10
Median length9
Mean length9.1578014
Min length9

Characters and Unicode

Total characters10330
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcounty__2
2nd rowcounty__1
3rd rowcounty__1
4th rowcounty__3
5th rowcounty__1

Common Values

ValueCountFrequency (%)
county__1 563
49.9%
county__3 193
 
17.1%
county__11 73
 
6.5%
county__6 58
 
5.1%
county__2 36
 
3.2%
county__4 35
 
3.1%
county__5 35
 
3.1%
county__10 30
 
2.7%
county__12 30
 
2.7%
county__13 20
 
1.8%
Other values (5) 55
 
4.9%

Length

2023-01-31T23:30:28.596850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county__1 563
49.9%
county__3 193
 
17.1%
county__11 73
 
6.5%
county__6 58
 
5.1%
county__2 36
 
3.2%
county__4 35
 
3.1%
county__5 35
 
3.1%
county__10 30
 
2.7%
county__12 30
 
2.7%
county__13 20
 
1.8%
Other values (5) 55
 
4.9%

Most occurring characters

ValueCountFrequency (%)
_ 2256
21.8%
c 1128
10.9%
o 1128
10.9%
u 1128
10.9%
n 1128
10.9%
t 1128
10.9%
y 1128
10.9%
1 814
 
7.9%
3 213
 
2.1%
2 66
 
0.6%
Other values (7) 213
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6768
65.5%
Connector Punctuation 2256
 
21.8%
Decimal Number 1306
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 814
62.3%
3 213
 
16.3%
2 66
 
5.1%
6 58
 
4.4%
5 51
 
3.9%
4 44
 
3.4%
0 30
 
2.3%
8 12
 
0.9%
9 12
 
0.9%
7 6
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
c 1128
16.7%
o 1128
16.7%
u 1128
16.7%
n 1128
16.7%
t 1128
16.7%
y 1128
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 2256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6768
65.5%
Common 3562
34.5%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2256
63.3%
1 814
 
22.9%
3 213
 
6.0%
2 66
 
1.9%
6 58
 
1.6%
5 51
 
1.4%
4 44
 
1.2%
0 30
 
0.8%
8 12
 
0.3%
9 12
 
0.3%
Latin
ValueCountFrequency (%)
c 1128
16.7%
o 1128
16.7%
u 1128
16.7%
n 1128
16.7%
t 1128
16.7%
y 1128
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 2256
21.8%
c 1128
10.9%
o 1128
10.9%
u 1128
10.9%
n 1128
10.9%
t 1128
10.9%
y 1128
10.9%
1 814
 
7.9%
3 213
 
2.1%
2 66
 
0.6%
Other values (7) 213
 
2.1%

precinct
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size17.6 KiB
precinct__128
 
2
precinct__1004
 
1
precinct__1015
 
1
precinct__1014
 
1
precinct__1011
 
1
Other values (1122)
1122 

Length

Max length14
Median length13
Mean length13.265957
Min length11

Characters and Unicode

Total characters14964
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1126 ?
Unique (%)99.8%

Sample

1st rowprecinct__3
2nd rowprecinct__4
3rd rowprecinct__5
4th rowprecinct__7
5th rowprecinct__8

Common Values

ValueCountFrequency (%)
precinct__128 2
 
0.2%
precinct__1004 1
 
0.1%
precinct__1015 1
 
0.1%
precinct__1014 1
 
0.1%
precinct__1011 1
 
0.1%
precinct__1010 1
 
0.1%
precinct__1007 1
 
0.1%
precinct__1006 1
 
0.1%
precinct__3 1
 
0.1%
precinct__1017 1
 
0.1%
Other values (1117) 1117
99.0%

Length

2023-01-31T23:30:28.675922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
precinct__128 2
 
0.2%
precinct__18 1
 
0.1%
precinct__8 1
 
0.1%
precinct__9 1
 
0.1%
precinct__11 1
 
0.1%
precinct__13 1
 
0.1%
precinct__50 1
 
0.1%
precinct__15 1
 
0.1%
precinct__19 1
 
0.1%
precinct__5 1
 
0.1%
Other values (1117) 1117
99.0%

Most occurring characters

ValueCountFrequency (%)
c 2256
15.1%
_ 2256
15.1%
p 1128
7.5%
r 1128
7.5%
e 1128
7.5%
i 1128
7.5%
n 1128
7.5%
t 1128
7.5%
1 765
 
5.1%
2 387
 
2.6%
Other values (8) 2532
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9024
60.3%
Decimal Number 3684
24.6%
Connector Punctuation 2256
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 765
20.8%
2 387
10.5%
3 376
10.2%
4 368
10.0%
7 308
8.4%
0 302
 
8.2%
8 301
 
8.2%
6 299
 
8.1%
5 290
 
7.9%
9 288
 
7.8%
Lowercase Letter
ValueCountFrequency (%)
c 2256
25.0%
p 1128
12.5%
r 1128
12.5%
e 1128
12.5%
i 1128
12.5%
n 1128
12.5%
t 1128
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9024
60.3%
Common 5940
39.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2256
38.0%
1 765
 
12.9%
2 387
 
6.5%
3 376
 
6.3%
4 368
 
6.2%
7 308
 
5.2%
0 302
 
5.1%
8 301
 
5.1%
6 299
 
5.0%
5 290
 
4.9%
Latin
ValueCountFrequency (%)
c 2256
25.0%
p 1128
12.5%
r 1128
12.5%
e 1128
12.5%
i 1128
12.5%
n 1128
12.5%
t 1128
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 2256
15.1%
_ 2256
15.1%
p 1128
7.5%
r 1128
7.5%
e 1128
7.5%
i 1128
7.5%
n 1128
7.5%
t 1128
7.5%
1 765
 
5.1%
2 387
 
2.6%
Other values (8) 2532
16.9%

population
Real number (ℝ)

Distinct1036
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3193.1285
Minimum7
Maximum32575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:28.770067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile334.3
Q11679.5
median2823.5
Q34202.25
95-th percentile7211.1
Maximum32575
Range32568
Interquartile range (IQR)2522.75

Descriptive statistics

Standard deviation2440.0146
Coefficient of variation (CV)0.76414543
Kurtosis32.867544
Mean3193.1285
Median Absolute Deviation (MAD)1241.5
Skewness3.5672661
Sum3601849
Variance5953671.2
MonotonicityNot monotonic
2023-01-31T23:30:28.872672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1746 4
 
0.4%
4014 3
 
0.3%
2052 3
 
0.3%
2826 3
 
0.3%
2103 3
 
0.3%
4607 3
 
0.3%
2109 2
 
0.2%
3291 2
 
0.2%
188 2
 
0.2%
542 2
 
0.2%
Other values (1026) 1101
97.6%
ValueCountFrequency (%)
7 1
0.1%
9 1
0.1%
18 1
0.1%
25 1
0.1%
33 1
0.1%
36 1
0.1%
44 1
0.1%
53 1
0.1%
55 1
0.1%
66 2
0.2%
ValueCountFrequency (%)
32575 1
0.1%
30354 1
0.1%
17369 1
0.1%
12812 1
0.1%
12439 1
0.1%
12044 1
0.1%
11528 1
0.1%
10867 1
0.1%
10429 1
0.1%
10373 1
0.1%

votes_for_president
Real number (ℝ)

Distinct1007
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2272.0621
Minimum5
Maximum23433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:28.985283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile216.8
Q11116.25
median1946.5
Q32981.25
95-th percentile5454.45
Maximum23433
Range23428
Interquartile range (IQR)1865

Descriptive statistics

Standard deviation1791.0948
Coefficient of variation (CV)0.78831246
Kurtosis23.640241
Mean2272.0621
Median Absolute Deviation (MAD)940.5
Skewness3.0065337
Sum2562886
Variance3208020.7
MonotonicityNot monotonic
2023-01-31T23:30:29.088394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1665 3
 
0.3%
1996 3
 
0.3%
1077 3
 
0.3%
1757 3
 
0.3%
2011 3
 
0.3%
1417 3
 
0.3%
3322 3
 
0.3%
3132 2
 
0.2%
1662 2
 
0.2%
3239 2
 
0.2%
Other values (997) 1101
97.6%
ValueCountFrequency (%)
5 1
0.1%
7 1
0.1%
14 1
0.1%
15 1
0.1%
17 1
0.1%
18 1
0.1%
20 1
0.1%
32 1
0.1%
33 1
0.1%
47 1
0.1%
ValueCountFrequency (%)
23433 1
0.1%
18291 1
0.1%
11379 1
0.1%
9370 1
0.1%
8683 1
0.1%
8605 1
0.1%
8481 1
0.1%
8432 1
0.1%
8407 1
0.1%
8015 1
0.1%

registered_voters
Real number (ℝ)

Distinct1014
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2861.2828
Minimum7
Maximum28632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:29.215509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile297.35
Q11477.75
median2506.5
Q33760
95-th percentile6658.2
Maximum28632
Range28625
Interquartile range (IQR)2282.25

Descriptive statistics

Standard deviation2165.4536
Coefficient of variation (CV)0.75681215
Kurtosis28.299077
Mean2861.2828
Median Absolute Deviation (MAD)1140.5
Skewness3.2659599
Sum3227527
Variance4689189.3
MonotonicityNot monotonic
2023-01-31T23:30:29.330140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2324 4
 
0.4%
2880 3
 
0.3%
1575 3
 
0.3%
3111 3
 
0.3%
2345 3
 
0.3%
690 3
 
0.3%
1409 3
 
0.3%
3234 2
 
0.2%
1105 2
 
0.2%
3265 2
 
0.2%
Other values (1004) 1100
97.5%
ValueCountFrequency (%)
7 2
0.2%
16 1
0.1%
21 1
0.1%
24 1
0.1%
28 1
0.1%
36 1
0.1%
39 1
0.1%
43 1
0.1%
61 2
0.2%
67 1
0.1%
ValueCountFrequency (%)
28632 1
0.1%
25157 1
0.1%
15015 1
0.1%
11117 1
0.1%
10990 1
0.1%
10113 1
0.1%
10104 1
0.1%
10102 1
0.1%
9605 1
0.1%
9418 1
0.1%

all_ballot_measure_votes
Real number (ℝ)

Distinct994
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2186.5363
Minimum5
Maximum21967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:29.438255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile210.75
Q11068
median1877.5
Q32875.25
95-th percentile5233.5
Maximum21967
Range21962
Interquartile range (IQR)1807.25

Descriptive statistics

Standard deviation1713.8334
Coefficient of variation (CV)0.783812
Kurtosis21.258566
Mean2186.5363
Median Absolute Deviation (MAD)906
Skewness2.8370942
Sum2466413
Variance2937225
MonotonicityNot monotonic
2023-01-31T23:30:29.543407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600 4
 
0.4%
303 3
 
0.3%
1518 3
 
0.3%
1921 3
 
0.3%
1253 3
 
0.3%
2161 3
 
0.3%
1224 3
 
0.3%
1257 2
 
0.2%
981 2
 
0.2%
1980 2
 
0.2%
Other values (984) 1100
97.5%
ValueCountFrequency (%)
5 1
0.1%
6 1
0.1%
7 1
0.1%
14 1
0.1%
19 2
0.2%
20 1
0.1%
27 1
0.1%
31 1
0.1%
45 1
0.1%
49 1
0.1%
ValueCountFrequency (%)
21967 1
0.1%
16964 1
0.1%
10503 1
0.1%
8767 1
0.1%
8443 1
0.1%
8290 1
0.1%
8249 1
0.1%
7970 1
0.1%
7944 1
0.1%
7609 1
0.1%

total_ballots
Real number (ℝ)

Distinct997
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2290.3803
Minimum5
Maximum23585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:29.652015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile214.7
Q11127.5
median1975.5
Q33003.25
95-th percentile5487.6
Maximum23585
Range23580
Interquartile range (IQR)1875.75

Descriptive statistics

Standard deviation1804.0974
Coefficient of variation (CV)0.78768463
Kurtosis23.613545
Mean2290.3803
Median Absolute Deviation (MAD)944.5
Skewness3.0054279
Sum2583549
Variance3254767.4
MonotonicityNot monotonic
2023-01-31T23:30:29.758146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310 3
 
0.3%
1969 3
 
0.3%
671 3
 
0.3%
1665 3
 
0.3%
1941 3
 
0.3%
2704 3
 
0.3%
2717 3
 
0.3%
984 3
 
0.3%
2721 3
 
0.3%
1460 3
 
0.3%
Other values (987) 1098
97.3%
ValueCountFrequency (%)
5 1
0.1%
7 1
0.1%
14 1
0.1%
15 1
0.1%
19 2
0.2%
20 1
0.1%
32 1
0.1%
34 1
0.1%
47 1
0.1%
55 1
0.1%
ValueCountFrequency (%)
23585 1
0.1%
18448 1
0.1%
11460 1
0.1%
9469 1
0.1%
8727 1
0.1%
8707 1
0.1%
8550 1
0.1%
8504 1
0.1%
8480 1
0.1%
8132 1
0.1%
Distinct845
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean871.13475
Minimum0
Maximum8868
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:29.885262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93.35
Q1385.75
median677
Q31113
95-th percentile2327
Maximum8868
Range8868
Interquartile range (IQR)727.25

Descriptive statistics

Standard deviation771.14583
Coefficient of variation (CV)0.88521992
Kurtosis15.19188
Mean871.13475
Median Absolute Deviation (MAD)343
Skewness2.7592766
Sum982640
Variance594665.89
MonotonicityNot monotonic
2023-01-31T23:30:29.996872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 5
 
0.4%
296 4
 
0.4%
612 4
 
0.4%
218 4
 
0.4%
472 4
 
0.4%
417 4
 
0.4%
621 4
 
0.4%
717 4
 
0.4%
937 4
 
0.4%
885 3
 
0.3%
Other values (835) 1088
96.5%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 2
0.2%
6 1
0.1%
9 1
0.1%
10 1
0.1%
12 1
0.1%
13 1
0.1%
15 1
0.1%
18 1
0.1%
ValueCountFrequency (%)
8868 1
0.1%
6434 1
0.1%
4442 1
0.1%
4368 1
0.1%
4343 1
0.1%
4231 1
0.1%
4089 1
0.1%
4084 1
0.1%
4022 1
0.1%
3976 1
0.1%

votes_for_ballot_measure
Real number (ℝ)

Distinct938
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1315.4016
Minimum4
Maximum13099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:30.110975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile118.35
Q1607
median1181
Q31795.75
95-th percentile3040.85
Maximum13099
Range13095
Interquartile range (IQR)1188.75

Descriptive statistics

Standard deviation1006.4258
Coefficient of variation (CV)0.76510917
Kurtosis22.856239
Mean1315.4016
Median Absolute Deviation (MAD)600.5
Skewness2.7772565
Sum1483773
Variance1012892.9
MonotonicityNot monotonic
2023-01-31T23:30:30.225079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1360 5
 
0.4%
54 4
 
0.4%
262 4
 
0.4%
5 3
 
0.3%
2187 3
 
0.3%
860 3
 
0.3%
1108 3
 
0.3%
252 3
 
0.3%
1077 3
 
0.3%
1188 3
 
0.3%
Other values (928) 1094
97.0%
ValueCountFrequency (%)
4 1
 
0.1%
5 3
0.3%
11 1
 
0.1%
13 2
0.2%
15 1
 
0.1%
17 1
 
0.1%
21 1
 
0.1%
23 1
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
13099 1
0.1%
10530 1
0.1%
6608 1
0.1%
4777 1
0.1%
4639 1
0.1%
4421 1
0.1%
4399 1
0.1%
4257 1
0.1%
4160 1
0.1%
4159 1
0.1%

probability_race_G
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.525046
Minimum12.624398
Maximum97.291262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:30.334178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.624398
5-th percentile24.681124
Q160.831845
median76.787905
Q386.103716
95-th percentile93.023023
Maximum97.291262
Range84.666864
Interquartile range (IQR)25.271871

Descriptive statistics

Standard deviation20.930484
Coefficient of variation (CV)0.29678086
Kurtosis0.25647807
Mean70.525046
Median Absolute Deviation (MAD)11.182461
Skewness-1.1028769
Sum79552.252
Variance438.08515
MonotonicityNot monotonic
2023-01-31T23:30:30.436289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.39988999 2
 
0.2%
79.70890474 1
 
0.1%
95.37437722 1
 
0.1%
70.12144213 1
 
0.1%
92.83831217 1
 
0.1%
86.08976157 1
 
0.1%
81.8204784 1
 
0.1%
45.59614355 1
 
0.1%
91.15649867 1
 
0.1%
64.93073422 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
12.62439807 1
0.1%
12.83978398 1
0.1%
12.93936414 1
0.1%
13.11453416 1
0.1%
13.28379287 1
0.1%
13.51158301 1
0.1%
13.64630468 1
0.1%
13.8028169 1
0.1%
13.98593407 1
0.1%
14 1
0.1%
ValueCountFrequency (%)
97.29126214 1
0.1%
96.88888889 1
0.1%
96.58990632 1
0.1%
95.91141869 1
0.1%
95.90935673 1
0.1%
95.87760252 1
0.1%
95.82106455 1
0.1%
95.70247934 1
0.1%
95.52608213 1
0.1%
95.37437722 1
0.1%

probability_race_P
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6393131
Minimum0.17895772
Maximum32.405448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:30.565951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.17895772
5-th percentile1.0579849
Q11.7171581
median2.7479422
Q34.6599374
95-th percentile9.1946897
Maximum32.405448
Range32.226491
Interquartile range (IQR)2.9427793

Descriptive statistics

Standard deviation2.8518905
Coefficient of variation (CV)0.78363428
Kurtosis13.125879
Mean3.6393131
Median Absolute Deviation (MAD)1.2227093
Skewness2.616061
Sum4105.1451
Variance8.1332793
MonotonicityNot monotonic
2023-01-31T23:30:30.688133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.419141914 2
 
0.2%
3.855366612 1
 
0.1%
1.151601423 1
 
0.1%
1.880455408 1
 
0.1%
1.306198656 1
 
0.1%
2.872370266 1
 
0.1%
3.870026849 1
 
0.1%
4.797000536 1
 
0.1%
1.691083452 1
 
0.1%
6.727488621 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
0.1789577188 1
0.1%
0.2112760835 1
0.1%
0.2285714286 1
0.1%
0.2594594595 1
0.1%
0.276371308 1
0.1%
0.3333333333 1
0.1%
0.3957983193 1
0.1%
0.4340949033 1
0.1%
0.436376971 1
0.1%
0.4446227929 1
0.1%
ValueCountFrequency (%)
32.4054484 1
0.1%
19.97960445 1
0.1%
18.54166667 1
0.1%
16.88769231 1
0.1%
16.76573321 1
0.1%
16.59680851 1
0.1%
16.55693614 1
0.1%
16.4782005 1
0.1%
15.68670592 1
0.1%
15.4 1
0.1%

probability_race_O
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.018978
Minimum0.359375
Maximum85.301868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:30.804827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.359375
5-th percentile2.071845
Q16.1305323
median12.037601
Q322.253565
95-th percentile58.407436
Maximum85.301868
Range84.942493
Interquartile range (IQR)16.123032

Descriptive statistics

Standard deviation17.333284
Coefficient of variation (CV)0.96194602
Kurtosis2.2148257
Mean18.018978
Median Absolute Deviation (MAD)7.1182932
Skewness1.6478552
Sum20325.407
Variance300.44273
MonotonicityNot monotonic
2023-01-31T23:30:30.906466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.349284928 2
 
0.2%
12.82574511 1
 
0.1%
1.544483986 1
 
0.1%
26.00189753 1
 
0.1%
2.914115011 1
 
0.1%
6.63884993 1
 
0.1%
8.251037344 1
 
0.1%
39.98125335 1
 
0.1%
4.200775352 1
 
0.1%
20.68711656 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
0.359375 1
0.1%
0.6699029126 1
0.1%
0.6812933025 1
0.1%
0.6914393227 1
0.1%
0.7152542373 1
0.1%
0.8156370656 1
0.1%
0.8184263618 1
0.1%
0.8524822695 1
0.1%
0.8567543064 1
0.1%
0.8847884788 1
0.1%
ValueCountFrequency (%)
85.30186824 1
0.1%
84.98394864 1
0.1%
84.52621118 1
0.1%
81.04196495 1
0.1%
79.39015817 1
0.1%
79.021398 1
0.1%
77.88989716 1
0.1%
77.53693694 1
0.1%
77.33277311 1
0.1%
75.44514768 1
0.1%

gender
Real number (ℝ)

Distinct1120
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45403753
Minimum0.30934744
Maximum0.66666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:31.018701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.30934744
5-th percentile0.39704392
Q10.4366819
median0.45321267
Q30.47081688
95-th percentile0.5145685
Maximum0.66666667
Range0.35731922
Interquartile range (IQR)0.034134978

Descriptive statistics

Standard deviation0.035210042
Coefficient of variation (CV)0.077548747
Kurtosis3.7114376
Mean0.45403753
Median Absolute Deviation (MAD)0.017184763
Skewness0.60267939
Sum512.15434
Variance0.0012397471
MonotonicityNot monotonic
2023-01-31T23:30:31.135413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 3
 
0.3%
0.4833333333 2
 
0.2%
0.4382120947 2
 
0.2%
0.4049773756 2
 
0.2%
0.4834983498 2
 
0.2%
0.4820143885 2
 
0.2%
0.4042553191 2
 
0.2%
0.455874292 1
 
0.1%
0.5150665732 1
 
0.1%
0.4801219512 1
 
0.1%
Other values (1110) 1110
98.4%
ValueCountFrequency (%)
0.3093474427 1
0.1%
0.3333333333 1
0.1%
0.3369489153 1
0.1%
0.3395061728 1
0.1%
0.347826087 1
0.1%
0.3536201469 1
0.1%
0.3560732113 1
0.1%
0.356223176 1
0.1%
0.3644859813 1
0.1%
0.3652571429 1
0.1%
ValueCountFrequency (%)
0.6666666667 1
0.1%
0.6619047619 1
0.1%
0.60844185 1
0.1%
0.578026592 1
0.1%
0.5759493671 1
0.1%
0.5729278794 1
0.1%
0.5729094679 1
0.1%
0.5700123916 1
0.1%
0.5665733707 1
0.1%
0.5659670165 1
0.1%

age
Real number (ℝ)

Distinct1126
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.801735
Minimum21.342016
Maximum80.626814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:31.260114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21.342016
5-th percentile41.189509
Q146.281344
median49.498018
Q354.354916
95-th percentile64.116548
Maximum80.626814
Range59.284798
Interquartile range (IQR)8.0735714

Descriptive statistics

Standard deviation7.2783514
Coefficient of variation (CV)0.14326974
Kurtosis2.0872523
Mean50.801735
Median Absolute Deviation (MAD)3.8837296
Skewness0.90915637
Sum57304.357
Variance52.9744
MonotonicityNot monotonic
2023-01-31T23:30:31.371813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.83388339 2
 
0.2%
47.33333333 2
 
0.2%
63.56126402 1
 
0.1%
75.6341637 1
 
0.1%
50.64136622 1
 
0.1%
63.43764003 1
 
0.1%
55.21793973 1
 
0.1%
54.86243902 1
 
0.1%
47.66041778 1
 
0.1%
49.78677142 1
 
0.1%
Other values (1116) 1116
98.9%
ValueCountFrequency (%)
21.34201586 1
0.1%
22.07744565 1
0.1%
29.71181658 1
0.1%
31.50817499 1
0.1%
32.05602782 1
0.1%
33.42409902 1
0.1%
33.58656873 1
0.1%
35.72340798 1
0.1%
36.49327628 1
0.1%
36.64036885 1
0.1%
ValueCountFrequency (%)
80.6268141 1
0.1%
77.99778024 1
0.1%
76.35649123 1
0.1%
76.27991603 1
0.1%
76.06119951 1
0.1%
75.99611046 1
0.1%
75.95130238 1
0.1%
75.77476341 1
0.1%
75.73183857 1
0.1%
75.6341637 1
0.1%

partisan_score
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.048325
Minimum6.2921348
Maximum93.587279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:31.489045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2921348
5-th percentile21.569819
Q132.948083
median45.058737
Q364.387111
95-th percentile86.623578
Maximum93.587279
Range87.295144
Interquartile range (IQR)31.439028

Descriptive statistics

Standard deviation20.44723
Coefficient of variation (CV)0.41687926
Kurtosis-0.83748597
Mean49.048325
Median Absolute Deviation (MAD)14.063133
Skewness0.45702476
Sum55326.511
Variance418.0892
MonotonicityNot monotonic
2023-01-31T23:30:31.593802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.85533553 2
 
0.2%
34.38150923 1
 
0.1%
24.63914591 1
 
0.1%
43.11385199 1
 
0.1%
20.1351755 1
 
0.1%
51.7379117 1
 
0.1%
35.65036585 1
 
0.1%
80.77343332 1
 
0.1%
21.93863405 1
 
0.1%
71.70320538 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
6.292134831 1
0.1%
7.333333333 1
0.1%
7.713508613 1
0.1%
10.88815789 1
0.1%
12.24271845 1
0.1%
13.7389581 1
0.1%
13.90104167 1
0.1%
14.21307506 1
0.1%
14.37736666 1
0.1%
16.02948403 1
0.1%
ValueCountFrequency (%)
93.58727929 1
0.1%
92.02262443 1
0.1%
91.77406593 1
0.1%
91.54828151 1
0.1%
91.19945504 1
0.1%
91.13586687 1
0.1%
91.02459954 1
0.1%
90.68150346 1
0.1%
90.5210643 1
0.1%
90.21242775 1
0.1%

turnout_score
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.150673
Minimum35.555027
Maximum87.277936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:31.699029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35.555027
5-th percentile52.548055
Q161.31977
median68.842787
Q374.991062
95-th percentile82.158818
Maximum87.277936
Range51.722909
Interquartile range (IQR)13.671293

Descriptive statistics

Standard deviation9.200536
Coefficient of variation (CV)0.13500286
Kurtosis-0.40288584
Mean68.150673
Median Absolute Deviation (MAD)6.8599412
Skewness-0.30620058
Sum76873.959
Variance84.649864
MonotonicityNot monotonic
2023-01-31T23:30:31.812152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.54235424 2
 
0.2%
76.06285401 1
 
0.1%
85.2455516 1
 
0.1%
64.39658444 1
 
0.1%
78.48581031 1
 
0.1%
78.43377715 1
 
0.1%
76.94987805 1
 
0.1%
55.7557579 1
 
0.1%
78.59204893 1
 
0.1%
65.47190344 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
35.55502717 1
0.1%
36.64479638 1
0.1%
38.3744204 1
0.1%
38.453125 1
0.1%
43.3902439 1
0.1%
44.05452128 1
0.1%
44.53061224 1
0.1%
44.74691358 1
0.1%
46.90666667 1
0.1%
46.93772727 1
0.1%
ValueCountFrequency (%)
87.2779358 1
0.1%
87.2348378 1
0.1%
87.07242152 1
0.1%
85.80122549 1
0.1%
85.2455516 1
0.1%
85.24386097 1
0.1%
85.24225865 1
0.1%
85.21291209 1
0.1%
85.17746777 1
0.1%
84.97796818 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.341526
Minimum26.084028
Maximum87.490151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:31.917333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26.084028
5-th percentile30.621544
Q141.322887
median52.345843
Q363.576175
95-th percentile73.497219
Maximum87.490151
Range61.406122
Interquartile range (IQR)22.253288

Descriptive statistics

Standard deviation13.539658
Coefficient of variation (CV)0.25867908
Kurtosis-1.0559641
Mean52.341526
Median Absolute Deviation (MAD)11.137528
Skewness-0.0036096338
Sum59041.241
Variance183.32234
MonotonicityNot monotonic
2023-01-31T23:30:32.018474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.9534626 2
 
0.2%
39.92916436 1
 
0.1%
43.57815846 1
 
0.1%
59.83938815 1
 
0.1%
54.66679189 1
 
0.1%
32.58695652 1
 
0.1%
38.28273664 1
 
0.1%
60.14339623 1
 
0.1%
53.73111658 1
 
0.1%
43.47628537 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
26.08402822 1
0.1%
26.29037801 1
0.1%
26.4338697 1
0.1%
27.16449561 1
0.1%
27.16666667 1
0.1%
27.37171629 1
0.1%
27.45857988 1
0.1%
27.46582278 1
0.1%
27.53857622 1
0.1%
27.5403752 1
0.1%
ValueCountFrequency (%)
87.49015064 1
0.1%
85.03598616 1
0.1%
79.04618474 1
0.1%
78.32919255 1
0.1%
78.25890279 1
0.1%
77.49498069 1
0.1%
77.45218543 1
0.1%
77.43097643 1
0.1%
76.72222222 1
0.1%
76.67856419 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.338044
Minimum42.637566
Maximum80.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:32.131628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum42.637566
5-th percentile51.967778
Q159.548306
median65.965242
Q372.054278
95-th percentile76.15939
Maximum80.92
Range38.282434
Interquartile range (IQR)12.505972

Descriptive statistics

Standard deviation7.7709851
Coefficient of variation (CV)0.11893507
Kurtosis-0.70445384
Mean65.338044
Median Absolute Deviation (MAD)6.2316879
Skewness-0.34691481
Sum73701.313
Variance60.38821
MonotonicityNot monotonic
2023-01-31T23:30:32.250311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.35298622 2
 
0.2%
58.25897436 1
 
0.1%
54.91348089 1
 
0.1%
68.49473684 1
 
0.1%
54.9424693 1
 
0.1%
64.83962264 1
 
0.1%
59.71761851 1
 
0.1%
75.13302752 1
 
0.1%
55.97645697 1
 
0.1%
74.52766532 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
42.63756614 1
0.1%
43.77981172 1
0.1%
43.8356525 1
0.1%
45.4950495 1
0.1%
45.62985685 1
0.1%
45.89181287 1
0.1%
45.90598841 1
0.1%
45.95423497 1
0.1%
46.11520737 1
0.1%
46.24668705 1
0.1%
ValueCountFrequency (%)
80.92 1
0.1%
80.65811966 1
0.1%
79.82758621 1
0.1%
79.82055749 1
0.1%
79.40029112 1
0.1%
79.26666667 1
0.1%
78.97310127 1
0.1%
78.86900958 1
0.1%
78.69061758 1
0.1%
78.56091954 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.656213
Minimum16.777778
Maximum84.023923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:32.367418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.777778
5-th percentile31.71687
Q140.937106
median50.041017
Q361.866178
95-th percentile76.351955
Maximum84.023923
Range67.246146
Interquartile range (IQR)20.929072

Descriptive statistics

Standard deviation13.860495
Coefficient of variation (CV)0.26832193
Kurtosis-0.7834505
Mean51.656213
Median Absolute Deviation (MAD)10.046668
Skewness0.28995887
Sum58268.208
Variance192.11332
MonotonicityNot monotonic
2023-01-31T23:30:32.478519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.47700831 2
 
0.2%
42.67644346 1
 
0.1%
31.73804425 1
 
0.1%
48.19120459 1
 
0.1%
30.86288505 1
 
0.1%
55.76227209 1
 
0.1%
41.41368318 1
 
0.1%
73.96010782 1
 
0.1%
34.31342365 1
 
0.1%
69.40793145 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
16.77777778 1
0.1%
19.88888889 1
0.1%
20.10227273 1
0.1%
22.75496689 1
0.1%
24.08312958 1
0.1%
24.38690476 1
0.1%
24.92094862 1
0.1%
25.46048898 1
0.1%
25.4791901 1
0.1%
26.9721223 1
0.1%
ValueCountFrequency (%)
84.02392344 1
0.1%
83.22314675 1
0.1%
82.99650757 1
0.1%
82.65798046 1
0.1%
82.44084137 1
0.1%
82.02605863 1
0.1%
81.92650334 1
0.1%
81.88498623 1
0.1%
81.80501931 1
0.1%
81.74236253 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.991299
Minimum31.988636
Maximum84.714203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:32.607636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31.988636
5-th percentile44.239306
Q152.84646
median58.98082
Q364.581477
95-th percentile75.242356
Maximum84.714203
Range52.725566
Interquartile range (IQR)11.735017

Descriptive statistics

Standard deviation9.0845683
Coefficient of variation (CV)0.15399844
Kurtosis-0.1452161
Mean58.991299
Median Absolute Deviation (MAD)5.8934364
Skewness0.085139629
Sum66542.186
Variance82.529381
MonotonicityNot monotonic
2023-01-31T23:30:32.725743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.92631579 2
 
0.2%
56.21176905 1
 
0.1%
38.69379015 1
 
0.1%
54.49139579 1
 
0.1%
46.88617581 1
 
0.1%
63.70056101 1
 
0.1%
54.15228053 1
 
0.1%
67.9509434 1
 
0.1%
44.3452381 1
 
0.1%
72.53746513 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
31.98863636 1
0.1%
35.33333333 1
0.1%
36.26624294 1
0.1%
36.67428571 1
0.1%
36.78472222 1
0.1%
37.46283255 1
0.1%
37.66648045 1
0.1%
38.08707865 1
0.1%
38.14269275 1
0.1%
38.46232439 1
0.1%
ValueCountFrequency (%)
84.71420256 1
0.1%
84.45417515 1
0.1%
83.11043689 1
0.1%
82.0104712 1
0.1%
81.30115274 1
0.1%
81.30027548 1
0.1%
80.94402421 1
0.1%
80.68146718 1
0.1%
80.23311897 1
0.1%
80.10069444 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.268795
Minimum20.029652
Maximum86.166667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:32.839391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.029652
5-th percentile23.90223
Q133.602087
median43.953836
Q358.297176
95-th percentile73.600355
Maximum86.166667
Range66.137015
Interquartile range (IQR)24.695088

Descriptive statistics

Standard deviation15.48042
Coefficient of variation (CV)0.33457582
Kurtosis-0.91906744
Mean46.268795
Median Absolute Deviation (MAD)12.022771
Skewness0.35194515
Sum52191.201
Variance239.64341
MonotonicityNot monotonic
2023-01-31T23:30:32.953495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.91135734 2
 
0.2%
65.16454529 1
 
0.1%
53.57887223 1
 
0.1%
37.54302103 1
 
0.1%
44.65815177 1
 
0.1%
63.05399719 1
 
0.1%
65.18305542 1
 
0.1%
28.01725067 1
 
0.1%
46.0907225 1
 
0.1%
47.2574731 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
20.02965159 1
0.1%
20.20534224 1
0.1%
20.49708455 1
0.1%
20.65140845 1
0.1%
21.07883817 1
0.1%
21.39846006 1
0.1%
21.44175824 1
0.1%
21.49272727 1
0.1%
21.5 1
0.1%
21.51724138 1
0.1%
ValueCountFrequency (%)
86.16666667 1
0.1%
79.3396488 1
0.1%
78.84413085 1
0.1%
78.81965318 1
0.1%
78.6984127 1
0.1%
78.47639887 1
0.1%
78.46116323 1
0.1%
78.4405416 1
0.1%
78.04937898 1
0.1%
77.97250204 1
0.1%
Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.835081
Minimum14.758333
Maximum66.056437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:33.066598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14.758333
5-th percentile30.908109
Q141.486681
median46.772254
Q350.875261
95-th percentile58.832069
Maximum66.056437
Range51.298104
Interquartile range (IQR)9.3885796

Descriptive statistics

Standard deviation8.5638989
Coefficient of variation (CV)0.18684158
Kurtosis1.7405104
Mean45.835081
Median Absolute Deviation (MAD)4.5826913
Skewness-0.86479614
Sum51701.972
Variance73.340364
MonotonicityNot monotonic
2023-01-31T23:30:33.182704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.81939058 2
 
0.2%
53.11178749 1
 
0.1%
16.70235546 1
 
0.1%
48.01147228 1
 
0.1%
30.55522164 1
 
0.1%
37.20687237 1
 
0.1%
48.14995096 1
 
0.1%
41.27385445 1
 
0.1%
31.0365353 1
 
0.1%
46.61139896 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
14.75833333 1
0.1%
15.16359164 1
0.1%
15.87374302 1
0.1%
15.97965571 1
0.1%
16.15183616 1
0.1%
16.59303666 1
0.1%
16.60884277 1
0.1%
16.70235546 1
0.1%
16.86833333 1
0.1%
17.38596491 1
0.1%
ValueCountFrequency (%)
66.05643739 1
0.1%
65.54511949 1
0.1%
65.00319244 1
0.1%
64.92408377 1
0.1%
64.36871345 1
0.1%
63.55739194 1
0.1%
63.54777476 1
0.1%
63.26254682 1
0.1%
63.00212057 1
0.1%
62.96654719 1
0.1%

support_trump_score
Real number (ℝ)

Distinct1127
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.028615
Minimum21.089395
Maximum93.102273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.6 KiB
2023-01-31T23:30:33.286798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21.089395
5-th percentile28.694191
Q140.879822
median52.721322
Q362.803955
95-th percentile75.314984
Maximum93.102273
Range72.012877
Interquartile range (IQR)21.924133

Descriptive statistics

Standard deviation14.410655
Coefficient of variation (CV)0.27697555
Kurtosis-0.77404884
Mean52.028615
Median Absolute Deviation (MAD)10.91673
Skewness0.037715843
Sum58688.278
Variance207.66696
MonotonicityNot monotonic
2023-01-31T23:30:33.396898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.0132964 2
 
0.2%
59.02822357 1
 
0.1%
72.94575303 1
 
0.1%
59.03059273 1
 
0.1%
71.58527423 1
 
0.1%
42.4740533 1
 
0.1%
56.6102256 1
 
0.1%
31.0425876 1
 
0.1%
72.95197044 1
 
0.1%
30.35571941 1
 
0.1%
Other values (1117) 1117
99.0%
ValueCountFrequency (%)
21.08939527 1
0.1%
21.92020064 1
0.1%
21.95616171 1
0.1%
22.74872123 1
0.1%
23.52843483 1
0.1%
23.54673591 1
0.1%
23.5854251 1
0.1%
23.93156843 1
0.1%
23.94935972 1
0.1%
24.02316602 1
0.1%
ValueCountFrequency (%)
93.10227273 1
0.1%
89.38351031 1
0.1%
86.9205298 1
0.1%
84.8754386 1
0.1%
84.823401 1
0.1%
84.67074165 1
0.1%
84.66666667 1
0.1%
83.62135922 1
0.1%
82.74603175 1
0.1%
82.6773399 1
0.1%

Interactions

2023-01-31T23:30:24.204647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:32.535326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.701062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.567194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:39.852163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.333250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:44.559822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:47.150569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:49.249970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:51.399157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:54.102471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:56.284616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:58.539300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:00.693329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:03.566365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:05.767798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:07.881191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:09.958803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:12.987350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:15.268655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.535632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.741290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:21.997272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:24.300261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:32.628410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.794262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.661280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:39.948250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.424905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:44.647940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:47.239209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:49.344624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:51.490296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:54.214672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:56.383706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:58.631384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:00.791927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:03.663453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:05.860965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:07.972274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:10.060441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:13.095467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:15.357258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.626282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.832374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:22.094992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:24.405866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:32.726007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.898304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.761371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:40.048340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.526998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-01-31T23:30:07.498756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:09.582376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:11.843139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:14.869677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.146201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.323273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:21.601766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:23.829799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:27.101750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.426207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.238896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:39.548868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.062420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:44.268048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:46.885694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:48.983657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:51.127855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:53.803423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:56.018818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:58.244942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:00.417060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:03.252376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:05.463437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:07.587346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:09.673968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:11.938225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:14.975790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.249313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.419922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:21.693401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:23.926887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:27.197394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.517336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.354001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:39.648959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.155541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:44.371141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:46.979348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:49.076251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:51.219939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:53.900075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:56.107899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:58.340084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:00.512656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:03.346606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:05.562041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:07.688986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:09.764559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:12.040828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:15.084931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.343399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.526038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:21.792510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:24.020972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:27.287512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:34.603924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:37.459096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:39.750051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:42.243622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:44.462733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:47.062439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:49.157377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:51.305545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:53.996729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:56.191483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:29:58.434679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:00.602247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:03.443724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:05.658190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:07.775065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:09.857184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:12.880222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:15.172537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:17.438509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:19.627674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:21.892159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:30:24.105055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-31T23:30:33.528527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
votes_for_candidate_Ivotes_for_candidate_Upopulationvotes_for_presidentregistered_votersall_ballot_measure_votestotal_ballotsvotes_against_ballot_measurevotes_for_ballot_measureprobability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_scorecounty
votes_for_candidate_I1.0000.6410.9180.9070.9200.9040.9080.7600.944-0.0500.2650.1190.041-0.3120.1840.103-0.4460.0950.2270.1960.3910.190-0.2890.225
votes_for_candidate_U0.6411.0000.8240.8930.8430.8970.8930.9490.8180.4300.000-0.2100.1990.112-0.5210.424-0.476-0.504-0.485-0.4320.5810.1530.4370.220
population0.9180.8241.0000.9710.9950.9690.9710.8860.9650.0390.2530.0930.018-0.259-0.0640.074-0.342-0.078-0.025-0.0220.3740.256-0.0120.268
votes_for_president0.9070.8930.9711.0000.9821.0001.0000.9390.9780.1950.156-0.0470.112-0.124-0.1760.272-0.489-0.210-0.130-0.1200.5170.1910.0720.234
registered_voters0.9200.8430.9950.9821.0000.9810.9830.9090.9690.0680.2180.0640.053-0.221-0.0880.126-0.371-0.108-0.050-0.0540.4040.2460.0100.234
all_ballot_measure_votes0.9040.8970.9691.0000.9811.0001.0000.9410.9770.2020.155-0.0520.110-0.122-0.1840.278-0.493-0.218-0.139-0.1240.5250.1950.0790.239
total_ballots0.9080.8930.9711.0000.9831.0001.0000.9390.9780.1930.156-0.0460.112-0.124-0.1740.271-0.487-0.208-0.129-0.1200.5150.1900.0710.228
votes_against_ballot_measure0.7600.9490.8860.9390.9090.9410.9391.0000.8550.3010.019-0.1740.2140.046-0.3560.376-0.455-0.407-0.326-0.3220.5390.1680.2760.216
votes_for_ballot_measure0.9440.8180.9650.9780.9690.9770.9780.8551.0000.1210.2340.0380.046-0.225-0.0610.196-0.487-0.083-0.0100.0090.4860.216-0.0510.232
probability_race_G-0.0500.4300.0390.1950.0680.2020.1930.3010.1211.000-0.467-0.7500.3200.658-0.7870.798-0.522-0.771-0.748-0.6820.570-0.4140.6820.200
probability_race_P0.2650.0000.2530.1560.2180.1550.1560.0190.234-0.4671.0000.354-0.403-0.5620.316-0.4440.0740.4020.3450.431-0.0700.415-0.3140.152
probability_race_O0.119-0.2100.093-0.0470.064-0.052-0.046-0.1740.038-0.7500.3541.000-0.161-0.5680.547-0.6200.3580.6100.4940.392-0.3870.323-0.4480.244
gender0.0410.1990.0180.1120.0530.1100.1120.2140.0460.320-0.403-0.1611.0000.489-0.1430.427-0.262-0.193-0.137-0.3280.202-0.4140.1340.119
age-0.3120.112-0.259-0.124-0.221-0.122-0.1240.046-0.2250.658-0.562-0.5680.4891.000-0.5530.611-0.099-0.511-0.569-0.6430.092-0.7530.5640.183
partisan_score0.184-0.521-0.064-0.176-0.088-0.184-0.174-0.356-0.061-0.7870.3160.547-0.143-0.5531.000-0.5830.2540.8800.9770.866-0.4400.126-0.9590.243
turnout_score0.1030.4240.0740.2720.1260.2780.2710.3760.1960.798-0.444-0.6200.4270.611-0.5831.000-0.738-0.709-0.561-0.5480.748-0.3260.4480.160
probability_highest_education_high_school-0.446-0.476-0.342-0.489-0.371-0.493-0.487-0.455-0.487-0.5220.0740.358-0.262-0.0990.254-0.7381.0000.4540.1920.136-0.938-0.005-0.0480.288
support_tax_on_wealthy_score0.095-0.504-0.078-0.210-0.108-0.218-0.208-0.407-0.083-0.7710.4020.610-0.193-0.5110.880-0.7090.4541.0000.8710.782-0.6260.073-0.8030.131
support_progressive_taxation_score0.227-0.485-0.025-0.130-0.050-0.139-0.129-0.326-0.010-0.7480.3450.494-0.137-0.5690.977-0.5610.1920.8711.0000.882-0.3910.127-0.9620.251
support_cannabis_legalization_score0.196-0.432-0.022-0.120-0.054-0.124-0.120-0.3220.009-0.6820.4310.392-0.328-0.6430.866-0.5480.1360.7820.8821.000-0.3010.284-0.8830.283
probability_income_over_100k0.3910.5810.3740.5170.4040.5250.5150.5390.4860.570-0.070-0.3870.2020.092-0.4400.748-0.938-0.626-0.391-0.3011.0000.1420.2560.247
probability_children_in_household0.1900.1530.2560.1910.2460.1950.1900.1680.216-0.4140.4150.323-0.414-0.7530.126-0.326-0.0050.0730.1270.2840.1421.000-0.1300.201
support_trump_score-0.2890.437-0.0120.0720.0100.0790.0710.276-0.0510.682-0.314-0.4480.1340.564-0.9590.448-0.048-0.803-0.962-0.8830.256-0.1301.0000.271
county0.2250.2200.2680.2340.2340.2390.2280.2160.2320.2000.1520.2440.1190.1830.2430.1600.2880.1310.2510.2830.2470.2010.2711.000

Missing values

2023-01-31T23:30:27.457231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T23:30:27.933247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

votes_for_candidate_Ivotes_for_candidate_Ucountyprecinctpopulationvotes_for_presidentregistered_votersall_ballot_measure_votestotal_ballotsvotes_against_ballot_measurevotes_for_ballot_measureprobability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_score
01286.02974.0county__2precinct__351644313.04747.04023.04361.02216.01807.091.1564991.6910834.2007750.47747263.56126421.93863478.59204953.73111755.97645734.31342444.34523846.09072231.03653572.951970
1728.01120.0county__1precinct__426431892.02351.01839.01899.0861.0978.084.0772892.73285810.1957990.44475247.59604032.41108968.27405949.70567157.88252042.74121452.72204556.87659750.43131062.975240
21287.0480.0county__1precinct__528561804.02531.01739.01824.0500.01239.048.2875144.09299742.7265650.43238243.29788875.24379461.76102357.51194074.65591470.42611965.34029938.19328447.64925433.022761
31837.01833.0county__3precinct__743683702.04052.03523.03744.01767.01756.088.0923112.1000246.0248250.49602359.87394637.20535180.85683332.11184256.77620846.61403551.28630665.20370436.97928858.613304
4169.0409.0county__1precinct__8701584.0670.0576.0586.0277.0299.089.5155562.3140744.2770370.45629656.32740725.05185279.99407436.91766552.93089432.42964148.58383271.86976042.88922267.326347
5416.065.0county__4precinct__9809487.0748.0468.0497.0254.0214.046.13396512.5480383.4005410.41238548.71635987.87747056.91963176.56048475.46616578.76747379.46774222.12365644.95430128.510753
61201.01313.0county__1precinct__1139952559.03560.02493.02579.0887.01606.062.0213386.01396229.1696520.44681445.08135946.59162763.32253855.46696969.73906851.95658059.90037346.78982455.24001155.113213
71532.0921.0county__3precinct__1337422501.03368.02300.02527.0684.01616.061.3193584.66572928.9372540.44428848.12436762.17979763.52982653.32483074.22583563.17913860.65249433.77012544.64569242.091837
8566.0174.0county__6precinct__151179757.01093.0726.0763.0416.0310.043.10506610.1951223.8780490.44099448.33096784.76131364.12866068.17371771.12201678.86678775.67416725.15301551.08010829.402340
91394.01589.0county__1precinct__1639813048.03763.02981.03064.01205.01776.079.3450463.25865311.2536330.45442548.50620940.36274872.05918141.10133061.05128245.34335158.82074562.24414953.20664954.245745
votes_for_candidate_Ivotes_for_candidate_Ucountyprecinctpopulationvotes_for_presidentregistered_votersall_ballot_measure_votestotal_ballotsvotes_against_ballot_measurevotes_for_ballot_measureprobability_race_Gprobability_race_Pprobability_race_Ogenderagepartisan_scoreturnout_scoreprobability_highest_education_high_schoolsupport_tax_on_wealthy_scoresupport_progressive_taxation_scoresupport_cannabis_legalization_scoreprobability_income_over_100kprobability_children_in_householdsupport_trump_score
11181629.0222.0county__1precinct__146932911879.03135.01723.01905.0541.01182.016.3260731.14509965.4016660.51665647.65438890.21204458.17905257.43735976.01571881.18099865.00644132.54814847.73107932.572303
1119777.0217.0county__1precinct__147020521002.01700.0953.01013.0271.0682.044.7432999.22577339.0489690.39814847.22067978.50514452.83796358.60727371.04332166.18857167.20987036.84883143.07532532.718961
11202036.01495.0county__1precinct__147160223592.05106.03440.03630.01255.02185.060.2690436.50192227.3993710.44246648.24690160.27693460.07648061.59676073.30478656.95615462.20602237.62299745.50149748.021130
11211113.01629.0county__1precinct__147232662769.03008.02710.02783.01377.01333.092.7591101.3640762.2560460.47969159.69987128.96808581.68439733.21662950.94686237.50730849.19876672.28905535.91003663.249107
1122488.0274.0county__1precinct__14731682780.01340.0755.0790.0224.0531.061.5106385.94430527.4173970.39837444.85490960.03314651.91932557.02846372.86951560.10942462.54206238.37444747.75648339.669197
11232994.06217.0county__2precinct__1474124399370.011117.08767.09469.04368.04399.084.2895231.74864611.0001690.47576854.12264225.00795170.58623058.33182161.78472738.81551249.64112741.29252644.31469971.362983
11241251.01420.0county__1precinct__147732392690.03033.02614.02706.01156.01458.090.7776331.6085833.1284140.47626852.80786734.64044279.35468129.96702652.99397842.80476756.28958576.58472146.17074856.223310
1125974.0580.0county__3precinct__147820001577.01808.01512.01595.0498.01014.082.0267161.81299111.0880040.47145152.13200654.52069174.42640136.94514866.66639259.79008463.36919855.54377638.49894540.279536
11261686.0655.0county__1precinct__148036922383.03018.02316.02396.0525.01791.063.5790827.01937921.6389280.41514841.30837167.61902160.66799546.54166773.41893366.28160969.79511544.73793147.35574732.483046
1127865.0273.0county__1precinct__148223591158.02019.01106.01168.0369.0737.033.7373604.87248358.1391500.45413940.18657782.18523551.57226070.76231474.17688768.39042065.69814730.59240850.21464142.653412